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Article: Bilinear probabilistic principal component analysis

TitleBilinear probabilistic principal component analysis
Authors
KeywordsBilinear systems
Data reduction
Parameter estimation
Principal component analysis
Probability
Issue Date2012
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=72
Citation
IEEE Transactions on Neural Networks and Learning Systems, 2012, v. 23 n. 3, p. 492-503 How to Cite?
Abstract
Probabilistic principal component analysis (PPCA) is a popular linear latent variable model for performing dimension reduction on 1-D data in a probabilistic manner. However, when used on 2-D data such as images, PPCA suffers from the curse of dimensionality due to the subsequently large number of model parameters. To overcome this problem, we propose in this paper a novel probabilistic model on 2-D data called bilinear PPCA (BPPCA). This allows the establishment of a closer tie between BPPCA and its nonprobabilistic counterpart. Moreover, two efficient parameter estimation algorithms for fitting BPPCA are also developed. Experiments on a number of 2-D synthetic and real-world data sets show that BPPCA is more accurate than existing probabilistic and nonprobabilistic dimension reduction methods.
Persistent Identifierhttp://hdl.handle.net/10722/146419
ISSN
2013 Impact Factor: 4.370
2013 SCImago Journal Rankings: 1.309
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhao, Jen_US
dc.contributor.authorYu, PLHen_US
dc.contributor.authorKwok, JTen_US
dc.date.accessioned2012-04-24T07:52:49Z-
dc.date.available2012-04-24T07:52:49Z-
dc.date.issued2012en_US
dc.identifier.citationIEEE Transactions on Neural Networks and Learning Systems, 2012, v. 23 n. 3, p. 492-503en_US
dc.identifier.issn2162-237X-
dc.identifier.urihttp://hdl.handle.net/10722/146419-
dc.description.abstractProbabilistic principal component analysis (PPCA) is a popular linear latent variable model for performing dimension reduction on 1-D data in a probabilistic manner. However, when used on 2-D data such as images, PPCA suffers from the curse of dimensionality due to the subsequently large number of model parameters. To overcome this problem, we propose in this paper a novel probabilistic model on 2-D data called bilinear PPCA (BPPCA). This allows the establishment of a closer tie between BPPCA and its nonprobabilistic counterpart. Moreover, two efficient parameter estimation algorithms for fitting BPPCA are also developed. Experiments on a number of 2-D synthetic and real-world data sets show that BPPCA is more accurate than existing probabilistic and nonprobabilistic dimension reduction methods.-
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=72en_US
dc.relation.ispartofIEEE Transactions on Neural Networks and Learning Systemsen_US
dc.rightsIEEE Transactions on Neural Networks and Learning Systems. Copyright © IEEE.-
dc.rights©2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectBilinear systems-
dc.subjectData reduction-
dc.subjectParameter estimation-
dc.subjectPrincipal component analysis-
dc.subjectProbability-
dc.titleBilinear probabilistic principal component analysisen_US
dc.typeArticleen_US
dc.identifier.emailYu, PLH: plhyu@hku.hken_US
dc.identifier.authorityYu, PLH=rp00835en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/TNNLS.2012.2183006-
dc.identifier.hkuros199326en_US
dc.identifier.volume23en_US
dc.identifier.issue3en_US
dc.identifier.spage492en_US
dc.identifier.epage503en_US
dc.identifier.isiWOS:000302705100010-
dc.publisher.placeUnited States-

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